arXiv (All)
open access
Event-Centric Memory with Query-Aware Graph Augmentation for Long-Term Conversational Agents
For persistent and personalized conversational agents, memory systems can enable them to remember, update, and reason over long histories by storing past interactions and retrieving relevant information. Existing memory systems typically follow two paradigms: flat-structured memory and graph-based m
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Gated Memory: Admission-Controlled Memory Formation for Conversational AI#1248550
- Beyond Memory Construction: Rethinking Memory Access for LLM-based Conversational Agents#1117938
- Towards Continuous Cognition: A Human-Centric Model for AI Long-Term Conversational Memory#714062
- Entity-Memory Graph Retrieval Improves Evidence Coverage in Long-Conversation Question Answering#781203
- Entity-Memory Graph Retrieval Improves Evidence Coverage in Long-Conversation Question Answering#779828
Record · ID 1249200
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive